"While technology analysis is still new, it can provide some of the needed foundations for technology as a field of its own and answer such questions as'Can AI invent?.'" Not long ago, Dr. Stephen Thaler, a member of the scientific community, began claiming that his artificial intelligence (AI) machine, DABUS, was a bona fide inventor. The outcome so far has been that the claim has been rejected in most jurisdictions. A notable exception is South Africa, which accepted Thaler's patent application under "Formalities Examination" with DABUS as named inventor. The acceptance of the patent in South Africa and the evolution of the legal field opens the possibility of further assertions and challenges with respect to AI inventorship.

However, estimating treatment effects using EHR data poses several challenges, including time-varying confounding, repeated and temporally non-aligned measurements of covariates, treatment assignments and outcomes, and loss-to-follow-up due to dropout. Here, we develop the Subgroup Discovery for Longitudinal Data (SDLD) algorithm, a tree-based algorithm for discovering subgroups with heterogeneous treatment effects using longitudinal data by combining the generalized interaction tree algorithm, a general data-driven method for subgroup discovery, with longitudinal targeted maximum likelihood estimation. We apply the algorithm to EHR data to discover subgroups of people living with human immunodeficiency virus (HIV) who are at higher risk of weight gain when receiving dolutegravir-containing antiretroviral therapies (ARTs) versus when receiving non dolutegravir-containing ARTs. Key words: Causal Inference; Dolutegravir; Electronic health record; Heterogeneity of treatment effects; Longitudinal targeted maximum likelihood estimation; Machine learning; Recursive partitioning; Subgroup discovery.

Data-driven design and innovation is a process to reuse and provide valuable and useful information. However, existing semantic networks for design innovation is built on data source restricted to technological and scientific information. Besides, existing studies build the edges of a semantic network only on either statistical or semantic relationships, which is less likely to make full use of the benefits from both types of relationships and discover implicit knowledge for design innovation. Therefore, we constructed WikiLink, a semantic network based on Wikipedia. Combined weight which fuses both the statistic and semantic weights between concepts is introduced in WikiLink, and four algorithms are developed for inspiring new ideas. Evaluation experiments are undertaken and results show that the network is characterised by high coverage of terms, relationships and disciplines, which proves the network's effectiveness and usefulness. Then a demonstration and case study results indicate that WikiLink can serve as an idea generation tool for innovation in conceptual design. The source code of WikiLink and the backend data are provided open-source for more users to explore and build on.

Automated Machine Learning with ensembling (or AutoML with ensembling) seeks to automatically build ensembles of Deep Neural Networks (DNNs) to achieve qualitative predictions. Ensemble of DNNs are well known to avoid over-fitting but they are memory and time consuming approaches. Therefore, an ideal AutoML would produce in one single run time different ensembles regarding accuracy and inference speed. While previous works on AutoML focus to search for the best model to maximize its generalization ability, we rather propose a new AutoML to build a larger library of accurate and diverse individual models to then construct ensembles. First, our extensive benchmarks show asynchronous Hyperband is an efficient and robust way to build a large number of diverse models to combine them. Then, a new ensemble selection method based on a multi-objective greedy algorithm is proposed to generate accurate ensembles by controlling their computing cost. Finally, we propose a novel algorithm to optimize the inference of the DNNs ensemble in a GPU cluster based on allocation optimization. The produced AutoML with ensemble method shows robust results on two datasets using efficiently GPU clusters during both the training phase and the inference phase. Deep Neural networks (DNNs) are notoriously difficult to tune, train, and ensemble to achieve state-of-the-art results. Automatic machine learning with ensembling or "AutoML+ensembling" tools provide a simple interface to train and evaluate many ensembles of DNNs to achieve high accuracy by reducing overfitting. Nowadays, multiple researchers and practitioners have well understood the benefit of ensembling DNNs. Further, several winners and top performers on challenges routinely use ensembles to improve accuracy. However, ensembles of DNNs suffer from three main limitations to be widely deployed in research and industrial applications.

Cross-lingual summarization is the task of generating a summary in one language (e.g., English) for the given document(s) in a different language (e.g., Chinese). Under the globalization background, this task has attracted increasing attention of the computational linguistics community. Nevertheless, there still remains a lack of comprehensive review for this task. Therefore, we present the first systematic critical review on the datasets, approaches, and challenges in this field. Specifically, we carefully organize existing datasets and approaches according to different construction methods and solution paradigms, respectively. For each type of datasets or approaches, we thoroughly introduce and summarize previous efforts and further compare them with each other to provide deeper analyses. In the end, we also discuss promising directions and offer our thoughts to facilitate future research. This survey is for both beginners and experts in cross-lingual summarization, and we hope it will serve as a starting point as well as a source of new ideas for researchers and engineers interested in this area.

Like many team sports, basketball involves two groups of players who engage in collaborative and adversarial activities to win a game. Players and teams are executing various complex strategies to gain an advantage over their opponents. Defining, identifying, and analyzing different types of activities is an important task in sports analytics, as it can lead to better strategies and decisions by the players and coaching staff. The objective of this paper is to automatically recognize basketball group activities from tracking data representing locations of players and the ball during a game. We propose a novel deep learning approach for group activity recognition (GAR) in team sports called NETS. To efficiently model the player relations in team sports, we combined a Transformer-based architecture with LSTM embedding, and a team-wise pooling layer to recognize the group activity. Training such a neural network generally requires a large amount of annotated data, which incurs high labeling cost. To address scarcity of manual labels, we generate weak-labels and pretrain the neural network on a self-supervised trajectory prediction task. We used a large tracking data set from 632 NBA games to evaluate our approach. The results show that NETS is capable of learning group activities with high accuracy, and that self- and weak-supervised training in NETS have a positive impact on GAR accuracy.

This work proposes the use of Genetic Algorithms (GA) in tracing and recognizing the pericardium contour of the human heart using Computed Tomography (CT) images. We assume that each slice of the pericardium can be modelled by an ellipse, the parameters of which need to be optimally determined. An optimal ellipse would be one that closely follows the pericardium contour and, consequently, separates appropriately the epicardial and mediastinal fats of the human heart. Tracing and automatically identifying the pericardium contour aids in medical diagnosis. Usually, this process is done manually or not done at all due to the effort required. Besides, detecting the pericardium may improve previously proposed automated methodologies that separate the two types of fat associated to the human heart. Quantification of these fats provides important health risk marker information, as they are associated with the development of certain cardiovascular pathologies. Finally, we conclude that GA offers satisfiable solutions in a feasible amount of processing time.

Many U.S. metropolitan cities are notorious for their severe shortage of parking spots. To this end, we present a proactive prediction-driven optimization framework to dynamically adjust parking prices. We use state-of-the-art deep learning technologies such as neural ordinary differential equations (NODEs) to design our future parking occupancy rate prediction model given historical occupancy rates and price information. Owing to the continuous and bijective characteristics of NODEs, in addition, we design a one-shot price optimization method given a pre-trained prediction model, which requires only one iteration to find the optimal solution. In other words, we optimize the price input to the pre-trained prediction model to achieve targeted occupancy rates in the parking blocks. We conduct experiments with the data collected in San Francisco and Seattle for years. Our prediction model shows the best accuracy in comparison with various temporal or spatio-temporal forecasting models. Our one-shot optimization method greatly outperforms other black-box and white-box search methods in terms of the search time and always returns the optimal price solution.

We invite you to experience the distinctive style of Alisa Hotels Accra conference rooms and facilities designed to accommodate small to large events with a state of the art array of technology and catering services to make your event a total success. Would you prefer to share this page with others by linking to it? The most excellent way to explain the definition of search engine,is to say, it is a website or an online service that collects and organizes content from all over the internet. If you wish to locate information on the internet,you would enter a query about what it is that you are searching for,the search engine provides links to content and information that matches the query you are searching for. Search engines use powerful computer software that has the capability of searching through huge volumes of text or other data for specified keywords and then returning a list of files or documents where the keywords were found ranked in order of relevance. Search engines make life easier for users by tracking down massive on-line information on a wide variety of topics and are valuable on-line sources of secondary data.

NIR Calibration-Model Services Spectroscopy and Chemometrics News Weekly 33, 2022 NIRS NIR Spectroscopy MachineLearning Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software IoT Sensors QA QC Testing Quality LINK Spektroskopie und Chemometrie Neuigkeiten Wöchentlich 33, 2022 NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK Spettroscopia e Chemiometria Weekly News 33, 2022 NIRS NIR Spettroscopia MachineLearning analisi chimica Spettrale Spettrometro Chem IoT Sensore Attrezzatura analitica Laboratorio analisi prova qualità Analysesystem QualityControl LINK Near-Infrared Spectroscopy (NIRS) "Comparative Performance of NIR-Hyperspectral Imaging Systems" LINK "Near infrared spectroscopy calibration strategies to predict multiple nutritional parameters of pasture species from different functional groups" LINK "Near-infrared spectroscopy as a tool to assist Sargassum fusiforme quality grading: Harvest time discrimination and polyphenol prediction" LINK "Sensors : Using Vis-NIR Spectroscopy for Predicting Quality Compounds in Foods" LINK "Development of an amino acid sequence-dependent analytical method for peptides using near-infrared spectroscopy" LINK "NDT model study of crown pear based on near infrared spectroscopy" LINK "Analyzing the Water Confined in Hydrogel Using Near-Infrared Spectroscopy" LINK "Foods : Finite Element Analysis and Near-Infrared Hyperspectral Reflectance Imaging for the Determination of Blueberry Bruise Grading" LINK "Application of near infrared spectroscopy in sub-surface monitoring of petroleum contaminants in laboratory-prepared soils" LINK "Identification of multiple raisins by feature fusion combined with NIR spectroscopy" LINK " … of quality markers for quality control of Zanthoxylum nitidum using ultra-performance liquid chromatography coupled with near infrared spectroscopy" LINK "Karakterisasi Fitokimia Enkapsulasi Nira Tebu Powder dengan Menggunakan Varietas BL, PSDK-923, dan PSBM-901" LINK "Inside the Egg--Demonstrating Provenance Without the Cracking Using Near Infrared Spectroscopy" LINK "Organic resources from Madagascar: Dataset of chemical and near-infrared spectroscopy measurements" LINK "An alternative method for identification of industrial tomato hybrids using NIRS" LINK "Uniformity evaluation of stem distribution in cut tobacco and single cigarette by near infrared spectroscopy" LINK "A General and Scalable Vision Framework for Functional Near-Infrared Spectroscopy Classification" LINK "Near infrared spectroscopy for the pre-cure freezing discrimination of Montanera Iberian dry-cured lomito" LINK "Determination of Moisture and Protein Content in Living Mealworm Larvae (Tenebrio molitor L.) Using Near-Infrared Reflectance Spectroscopy (NIRS)" LINK "Towards Inline Prediction of Color Development for Wood Stained with Chemical Stains Using Near-Infrared Spectroscopy" LINK "Comparison Between Pure Component Modeling Approaches for Monitoring Pharmaceutical Powder Blends with Near-Infrared Spectroscopy in Continuous Manufacturing Schemes" LINK "Potential of NIRS technology for the determination of cannabinoid content in industrial hemp (Cannabis sativa L.)" LINK " A Variable Selection Method Based on Fast Nondominated Sorting Genetic Algorithm for Qualitative Discrimination of Near Infrared Spectroscopy" LINK "Scale invariance in fNIRS as a measurement of cognitive load" LINK "Quantification of Salicylates and Flavonoids in Poplar Bark and Leaves Based on IR, NIR, and Raman Spectra" LINK Infrared Spectroscopy (IR) and Near-Infrared Spectroscopy (NIR) "Near-infrared spectroscopy monitoring during endovascular treatment for acute ischaemic stroke" LINK "Keakuratan Teknologi Near Infrared Dalam Mengukur Dan Memetakan Bahan Organik Di Pulau Lombok" LINK "NearInfrared Spectroscopic Characterization of Cardiac and Renal Fibrosis in Fixed and Fresh Rat Tissue" LINK "Application of Fourier transform infrared spectroscopy (FTIR) techniques in the mid-IR (MIR) and near-IR (NIR) spectroscopy to determine n-alkane and long-chain alcohol contents in plant species and faecal samples" LINK Hyperspectral Imaging (HSI) "Detection Storage Time of Mild Bruise's Loquats Using Hyperspectral Imaging" LINK "Determination of plumpness for kernel of semen ziziphi spinosae use of hyperspectral transmittance imaging technology coupled with improved Otsu algorithm" LINK "Prediction of oil content in single maize kernel based on hyperspectral imaging and attention convolution neural network" LINK "Convolutional neural networks for mapping of lake sediment core particle size using hyperspectral imaging" LINK Spectral Imaging "Applied Sciences : Non-Invasive Monitoring of the Thermal and Morphometric Characteristics of Lettuce Grown in an Aeroponic System through Multispectral Image System" LINK Chemometrics and Machine Learning "Rapid quantification of goat milk adulteration with cow milk using Raman spectroscopy and chemometrics" LINK "Plants : Prediction of Oil Palm Yield Using Machine Learning in the Perspective of Fluctuating Weather and Soil Moisture Conditions: Evaluation of a Generic Workflow" LINK "Near Infrared Spectra Data Analysis by Using Machine Learning Algorithms" LINK "Applied Sciences : Deep-Learning Model Selection and Parameter Estimation from a Wind Power Farm in Taiwan" LINK "Predicting maize LAI in partial least square modeling by continuous wavelet transform and uninformative variable elimination from canopy spectral reflectance" LINK "Machine Learning Algorithms for Protein Physicochemical Component Prediction Using Near Infrared Spectroscopy in Chickpea Germplasm" LINK "NIR Validation and Calibration of Proximate components of available Corn Silage in Bangladesh." 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